Multiagent Model-based Credit Assignment for Continuous Control
This work addresses the challenge of enabling decentralized operation without communication for robotic systems, which is incremental as it builds on existing methods like PPO and credit assignment.
The paper tackles the problem of decentralized multiagent reinforcement learning for robotic continuous control by proposing a framework that combines cooperative multiagent PPO, game-theoretic credit assignment, and model-based RL, resulting in improved sample efficiency demonstrated on Mujoco locomotion tasks.
Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability among all the components of a robot. However, agents in the real world often operate in a decentralised fashion without communication due to latency requirements, limited power budgets and safety concerns. By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control. To this end, we first develop a cooperative multiagent PPO framework that allows for centralized optimisation during training and decentralised operation during execution. However, the system only receives a global reward signal which is not attributed towards each agent. To address this challenge, we further propose a generic game-theoretic credit assignment framework which computes agent-specific reward signals. Last but not least, we also incorporate a model-based RL module into our credit assignment framework, which leads to significant improvement in sample efficiency. We demonstrate the effectiveness of our framework on experimental results on Mujoco locomotion control tasks. For a demo video please visit: https://youtu.be/gFyVPm4svEY.